Integrating meteorological data in biomass prediction models

Diouf, A.A.; Hiernaux, P.; Brandt, M.; Faye, G.; Djaby, B.; Diop, M.B.; Ndione, J.A.; Tychon, B. Do Agrometeorological Data Improve Optical Satellite-Based Estimations of the Herbaceous Yield in Sahelian Semi-Arid Ecosystems? Remote Sens. 2016, 8, 668.

Quantitative estimates of forage availability at the end of the growing season in rangelands are helpful for pastoral livestock managers and for local, national and regional stakeholders in natural resource management. For this reason, remote sensing data such as the Fraction of Absorbed Photosynthetically Active Radiation (FAPAR) have been widely used to assess Sahelian plant productivity for about 40 years.

This study combines traditional FAPAR-based assessments with agrometeorological variables computed by the geospatial water balance program, GeoWRSI, using rainfall and potential evapotranspiration satellite gridded data to estimate the annual herbaceous yield in the semi-arid areas of Senegal.

It showed that a machine-learning model combining FAPAR seasonal metrics with various agrometeorological data provided better estimations of the in situ annual herbaceous yield (R2 = 0.69; RMSE = 483 kg·DM/ha) than models based exclusively on FAPAR metrics (R2 = 0.63; RMSE = 550 kg·DM/ha) or agrometeorological variables (R2 = 0.55; RMSE = 585 kg·DM/ha). All the models provided reasonable outputs and showed a decrease in the mean annual yield with increasing latitude, together with an increase in relative inter-annual variation. In particular, the additional use of agrometeorological information mitigated the saturation effects that characterize the plant indices of areas with high plant productivity.

The date of the onset of the growing season derived from smoothed FAPAR seasonal dynamics showed no significant relationship (0.05 p-level) with the annual herbaceous yield across the whole studied area. The date of the onset of rainfall was significantly related to the herbaceous yield and its inclusion in fodder biomass models could constitute a significant improvement in forecasting risks of a mass herbaceous deficit at an early stage of the year.

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Remote sensing of vegetation in drylands: Evaluating vegetation optical depth (VOD) using NDVI and in situ data over Sahel

Tian, F.; Brandt, M.; Liu, Y. Y.; Verger, A.; Tagesson, T.; Diouf, A. A.; Rasmussen, K.; Mbow, C.; Wang, Y.; Fensholt, R. Remote sensing of vegetation dynamics in drylands: Evaluating vegetation optical depth (VOD) using AVHRR NDVI and in situ green biomass data over West African Sahel. Remote Sensing of Environment 2016, 177, 265–276.

 

  •  A Long-term VOD dataset is evaluated against NDVI and in situ biomass observations.
  • Both VOD and NDVI reflect the spatio-temporal patterns of biomass in West Sahel.
  • VOD captures variations of woody plant foliage biomass better than NDVI.
  • VOD and NDVI seasonal metrics differ for optimal long-term monitoring of biomass.

Monitoring long-term biomass dynamics in drylands is of great importance for many environmental applications including land degradation and global carbon cycle modeling. Biomass has extensively been estimated based on the normalized difference vegetation index (NDVI) as a measure of the vegetation greenness. The vegetation optical depth (VOD) derived from satellite passive microwave observations is mainly sensitive to the water content in total aboveground vegetation layer. VOD therefore provides a complementary data source to NDVI for monitoring biomass dynamics in drylands, yet further evaluations based on ground measurements are needed for an improved understanding of the potential advantages.

In this study, we assess the capability of a long-term VOD dataset (1992–2011) to capture the temporal and spatial variability of in situ measured green biomass (herbaceous mass and woody plant foliage mass) in the semi-arid Senegalese Sahel.

Results show that the magnitude and peak time of VOD are sensitive to the woody plant foliage whereas NDVI seasonality is primarily governed by the green herbaceous vegetation stratum in the study area. Moreover, VOD is found to be more robust against typical NDVI drawbacks of saturation effect and dependence on plant structure (herbaceous and woody compositions) across the study area when used as a proxy for vegetation productivity. Finally, both VOD and NDVI well reflect the spatial and inter-annual dynamics of the in situ green biomass data; however, the seasonal metrics showing the highest degree of explained variance differ between the two data sources. While the observations in October (period of in situ data collection) perform best for VOD (r2 = 0.88), the small growing season integral (sensitive to recurrent vegetation) have the highest correlations for NDVI (r2 = 0.90).

Overall, in spite of the coarse resolution of 25 km, the study shows that VOD is an efficient proxy for estimating biomass of the entire vegetation stratum in the semi-arid Sahel and likely also in other dryland areas.

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AGU 2015

We had three presentations at this years AGU fall meeting in San Francisco. Find the posters and presentations as PDFs here (the copyright is with the authors):

Fodder Biomass Monitoring in Sahelian Rangelands Using Phenological Metrics from FAPAR Time Series

From: Diouf, A.A., Brandt, M., Verger, A., Jarroudi, M.E., Djaby, B., Fensholt, R., Ndione, J.A., Tychon, B., 2015. Fodder Biomass Monitoring in Sahelian Rangelands Using Phenological Metrics from FAPAR Time Series. Remote Sensing 7, 9122–9148. doi:10.3390/rs70709122

Livestock farming constitutes the most widespread human activity and the dominant land use in rangeland ecosystems. At a global scale, it contributes 40% of the agricultural gross domestic product, and provides income for more than 1.3 billion people and nourishment for at least 800 million food-insecure people. In particular for the West African Sahel, livestock constitutes the first renewable resource and is mainly characterized by an extensive use of pastures in rangelands.

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Since 1987 the Centre de Suivi Ecologique (CSE) operationally estimates the total annual biomass in Senegal in order to monitor the fodder availability of the national pastoral rangelands. Field data is collected along 1 km transects at 24 sites at the end of the wet season. Here, herbaceous and woody leaf biomass is measured and summed to the total available fodder biomass. This is done each year since 1987.

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Using a linear regression with satellite images, the field data is projected to whole Senegal and gives stakeholders an estimation on the quantity and distribution of fodder biomass. Between 1987 and 1999, this method was implemented using the seasonal integrated NDVI (i.e., seasonal weighted average) from the Advanced Very High Resolution Radiometer (AVHRR) of the National Oceanic and Atmospheric Administration (NOAA) satellites acquired in Local Area Coverage (LAC) format at the CSE receiving station in Dakar. Since 2000, the 1-km SPOT-VEGETATION NDVI have been used.

In this context, we developed a new operational system for monitoring total fodder biomass, including both herbaceous and woody leaf biomass. The proposed method is based on multiple linear regression models using phenological variables derived from the seasonal dynamics of the FAPAR SPOT-VEGETATION time series and ground measurements of total biomass production collected in different Sahelian ecosystems in Senegal over 15 years.

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A model with three variables – large seasonal integral (LINTG), length of growing season and end of season decreasing rate – performed best (MAE = 605 kg DM/ha; R² = 0.68) across Sahelian ecosystems in Senegal (data for the period 1999-2013). A model with annual maximum (PEAK) and start date of season showed similar performances (MAE = 625 kg DM/ha; R² = 0.64), allowing a timely estimation of forage availability. The subdivision of the study area using metrics related to ecosystem properties increased overall accuracy (MAE = 489.21 kg DM/ha; R² = 0.77). LINTG was the main explanatory variable for woody rangelands, whereas for areas dominated by herbaceous vegetation it was the PEAK metric. The proposed approach outperformed the established single-predictor model (MAE = 818 kg DM/ha and R² = 0.51) and should improve the operational monitoring of forage resources in Sahelian rangelands.

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In the future, such early warning models should enable stakeholders to take decisions as early as September (current year as biomass shortage) with regard to livestock by triggering protocols designed for livestock management (e.g., Opération de Sauvegarde du Bétail ) in Senegal.

see the full document here: MDPI

Text and Figures: A.A. Diouf; Fotos: M. Brandt

Ground- and satellite-based evidence of the biophysical mechanisms behind the greening Sahel

Making use of 27 years of ground measurements, we were able to find evidence of the role of trees and grass on the greening of the Senegalese Sahel. This was made possible by a close collaboration with our colleagues from the CSE, the Centre de Suivi Ecologique in Dakar. Moreover, woody species abundance data provided by Gray Tappan from 1983 shows changes in biodiversity over 30 years. We thus provide ground based evidences against the conventional view of irreversible degradation in the Sahel.

Keywords:

  • biodiversity;
  • biomass monitoring;
  • degradation;
  • greening;
  • Sahel;
  • vegetation change

Abstract

After a dry period with prolonged droughts in the 1970s and 1980s, recent scientific outcome suggests that the decades of abnormally dry conditions in the Sahel have been reversed by positive anomalies in rainfall. Various remote sensing studies observed a positive trend in vegetation greenness over the last decades which is known as the re-greening of the Sahel. However, little investment has been made in including long-term ground-based data collections to evaluate and better understand the biophysical mechanisms behind these findings. Thus, deductions on a possible increment in biomass remain speculative. Our aim is to bridge these gaps and give specifics on the biophysical background factors of the re-greening Sahel. Therefore, a trend analysis was applied on long time series (1987–2013) of satellite-based vegetation and rainfall data, as well as on ground-observations of leaf biomass of woody species, herb biomass, and woody species abundance in different ecosystems located in the Sahel zone of Senegal. We found that the positive trend observed in satellite vegetation time series (+36%) is caused by an increment of in situ measured biomass (+34%), which is highly controlled by precipitation (+40%). Whereas herb biomass shows large inter-annual fluctuations rather than a clear trend, leaf biomass of woody species has doubled within 27 years (+103%). This increase in woody biomass did not reflect on biodiversity with 11 of 16 woody species declining in abundance over the period. We conclude that the observed greening in the Senegalese Sahel is primarily related to an increasing tree cover that caused satellite-driven vegetation indices to increase with rainfall reversal.

Brandt, M., Mbow, C., Diouf, A.A., Verger, A., Samimi, C. & R. Fensholt (2015) Ground and satellite based evidence of the biophysical mechanisms behind the greening Sahel. Global Change Biology.

Local Vegetation Trends in the Sahel of Mali and Senegal Using Long Time Series FAPAR Satellite Products and Field Measurement (1982–2010)

We finally published an article dealing with local vegetation trends in the Sahel and data quality of long term time series (GEOV1 and GIMMS3g). It is published in the open access journal “Remote Sensing” and can be downloaded for free:

http://www.mdpi.com/2072-4292/6/3/2408

Brandt, Martin; Verger, Aleixandre; Diouf, Abdoul A.; Baret, Frédéric; Samimi, Cyrus. 2014. “Local Vegetation Trends in the Sahel of Mali and Senegal Using Long Time Series FAPAR Satellite Products and Field Measurement (1982–2010).” Remote Sens. 6, no. 3: 2408-2434.

Abstract: Local vegetation trends in the Sahel of Mali and Senegal from Geoland Version 1 (GEOV1) (5 km) and the third generation Global Inventory Modeling and Mapping Studies (GIMMS3g) (8 km) Fraction of Absorbed Photosynthetically Active Radiation (FAPAR) time series are studied over 29 years. For validation and interpretation of observed greenness trends, two methods are applied: (1) a qualitative approach using in-depth knowledge of the study areas and (2) a quantitative approach by time series of biomass observations and rainfall data. Significant greening trends from 1982 to 2010 are consistently observed in both GEOV1 and GIMMS3g FAPAR datasets. Annual rainfall increased significantly during the observed time period, explaining large parts of FAPAR variations at a regional scale. Locally, GEOV1 data reveals a heterogeneous pattern of vegetation change, which is confirmed by long-term ground data and site visits. The spatial variability in the observed vegetation trends in the Sahel area are mainly caused by varying tree- and land-cover, which are controlled by human impact, soil and drought resilience. A large proportion of the positive trends are caused by the increment in leaf biomass of woody species that has almost doubled since the 1980s due to a tree cover regeneration after a dry-period. This confirms the re-greening of the Sahel, however, degradation is also present and sometimes obscured by greening. GEOV1 as compared to GIMMS3g made it possible to better characterize the spatial pattern of trends and identify the degraded areas in the study region.

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